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plot(cars)

# install.packages("stringr")
library(stringr)
data<- read.csv("cars_ship_plane.csv", header = TRUE)

# Install and load necessary libraries if not already installed
if (!require("tidyverse")) 

Error: Incomplete expression: if (!require("tidyverse")) 

# # Install the tidyverse package if not already installed
# if (!requireNamespace("tidyverse", quietly = TRUE)) {
#   install.packages("tidyverse")
# }

# Load the tidyverse package
library(tidyverse)

# # Your data
# data <- read.csv("your_file.csv")

# Remove rows with empty values in Caption, Photo_URL, and Tags columns
df_cleaned <- data %>%
  drop_na(Caption, Photo_URL, Tags)
# Print the cleaned data frame
print(df_cleaned)

NA

# Install and load necessary libraries if not already installed
if (!require("tidyverse")) install.packages("tidyverse")

# Assuming 'data' is already loaded with your dataset

# Your existing code for extracting labels goes here

# Create a bar plot of label frequencies
label_counts <- table(df_cleaned$Label)
label_counts_df <- data.frame(Label = names(label_counts), Count = as.numeric(label_counts))

# Print label counts
print(label_counts_df)

# Plotting using ggplot2
library(ggplot2)
ggplot(label_counts_df, aes(x = Label, y = Count, fill = Label)) +
  geom_bar(stat = "identity") +
  labs(title = "Distribution of Labels",
       x = "Label",
       y = "Count") +
  theme_minimal()

# Load the data from CSV file
data2 <- df_cleaned

# Create a folder for images
output_folder_flickrimages <- "/CIS8398/flickerimages"
dir.create(output_folder_flickrimages, showWarnings = FALSE)

# Initialize a counter for the total number of images downloaded
total_images_downloaded <- 0

# Iterate through each URL and download the corresponding image
for (i in seq_along(data2$Photo_URL)) {
  url <- data2$Photo_URL[i]
  label <- data2$Label[i]
  photo_id <- data2$Photo_ID[i]

  # Create a subfolder for each label
  label_folder <- file.path(output_folder_flickrimages, label)
  dir.create(label_folder, showWarnings = FALSE)

  # Set the file path and name for the downloaded image
  file_path <- file.path(label_folder, paste0(photo_id, "_", label, ".jpg"))

  tryCatch({
    # Download the image and save it to the specified file path
    download.file(url, destfile = file_path, mode = "wb", quiet = TRUE)

    # Increment the total count of images downloaded
    total_images_downloaded <- total_images_downloaded + 1
  }, error = function(e) {
    # Print an error message if the download fails
    cat(sprintf("Error downloading image %d from URL %s: %s\n", i, url, e$message))
  })
}

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# Print the final count of total images downloaded
cat("Total images downloaded:", total_images_downloaded, "\n")

Total images downloaded: 3666 

# Set your dataset paths on local desktop
car_dir <- "C:/CIS8398/flickerimages/car"
ship_dir <- "C:/CIS8398/flickerimages/ship"
plane_dir <- "C:/CIS8398/flickerimages/plane"

# Set your output paths for train and test on local desktop
train_dir <- "C:/CIS8398/flickerimages2/train"
test_dir <- "C:/CIS8398/flickerimages2/test"

# Function to split images into train and test
split_images <- function(vehicle_dir, vehicle_name) {
  # Get all image files in the vehicle folder
  image_files <- list.files(vehicle_dir, pattern = "\\.(jpg|jpeg|png)$", full.names = TRUE)

  # Randomly shuffle the image files
  set.seed(123)  # Set seed for reproducibility
  shuffled_files <- sample(image_files)

  # Determine the split point for train and test
  split_point <- round(0.8 * length(shuffled_files))

  # Create subfolders in train and test for the vehicle
  vehicle_train_dir <- file.path(train_dir, vehicle_name)
  vehicle_test_dir <- file.path(test_dir, vehicle_name)

  dir.create(vehicle_train_dir, showWarnings = FALSE, recursive = TRUE)
  dir.create(vehicle_test_dir, showWarnings = FALSE, recursive = TRUE)

  # Copy images to train folder
  file.copy(shuffled_files[1:split_point], vehicle_train_dir)

  # Copy images to test folder
  file.copy(shuffled_files[(split_point + 1):length(shuffled_files)], vehicle_test_dir)
}

# Split images for each vehicle using local paths
split_images(car_dir, "car")

  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [37] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [73] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[109] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[145] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[217] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

split_images(ship_dir, "ship")

  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [37] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [73] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[109] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[145] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[217] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

split_images(plane_dir, "plane")

  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [37] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
 [73] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[109] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[145] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[217] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

# Print a message indicating successful split
cat("Images successfully split into train and test folders.\n")

Images successfully split into train and test folders.

library(keras)
library(reticulate)
library(tidyverse)

# Set your dataset paths for car, ship, and plane
train_dir <- "C:/CIS8398/flickerimages/train"
test_dir <- "C:/CIS8398/flickerimages/test"

# Building your network
model_vehicle <- keras_model_sequential() %>%
  layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = "relu",
                input_shape = c(150, 150, 3)) %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_flatten() %>%
  layer_dense(units = 512, activation = "relu") %>%
  layer_dense(units = 3, activation = "softmax")  # 3 output units for car, ship, plane

model_vehicle %>% compile(
  optimizer = "adam",
  loss = "categorical_crossentropy",  # Use categorical crossentropy for multi-class classification
  metrics = c("accuracy")
)

# Data preprocessing
train_datagen <- image_data_generator(rescale = 1/255)
test_datagen <- image_data_generator(rescale = 1/255)

train_generator <- flow_images_from_directory(
  train_dir,
  train_datagen,
  target_size = c(150, 150),
  batch_size = 30,
  class_mode = "categorical",
  classes = c("car", "ship", "plane")  # Specify classes for multi-class
)

Found 2932 images belonging to 3 classes.

test_generator <- flow_images_from_directory(
  test_dir,
  test_datagen,
  target_size = c(150, 150),
  batch_size = 30,
  class_mode = "categorical",
  classes = c("car", "ship", "plane")  # Specify classes for multi-class
)

Found 734 images belonging to 3 classes.

# Calculate the number of validation steps
validation_steps <- ceiling(734 / 30)

library(ggplot2)

# Fit the model to the data using the generator
history_vehicle <- model_vehicle %>%
  fit_generator(
    train_generator,
    steps_per_epoch = as.integer(90),  # Replace with steps per epoch
    epochs = 10,
    validation_data = test_generator,
    validation_steps = as.integer(validation_steps)  # Replace with validation steps
  )

Warning: `fit_generator` is deprecated. Use `fit` instead, it now accept generators.

Epoch 1/10

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90/90 [==============================] - 108s 1s/step - loss: 0.9829 - accuracy: 0.5267 - val_loss: 0.8984 - val_accuracy: 0.5777
Epoch 2/10

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90/90 [==============================] - 95s 1s/step - loss: 0.8399 - accuracy: 0.6074

90/90 [==============================] - 105s 1s/step - loss: 0.8399 - accuracy: 0.6074 - val_loss: 0.7858 - val_accuracy: 0.6567
Epoch 3/10

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90/90 [==============================] - 94s 1s/step - loss: 0.7475 - accuracy: 0.6694

90/90 [==============================] - 104s 1s/step - loss: 0.7475 - accuracy: 0.6694 - val_loss: 0.7270 - val_accuracy: 0.6962
Epoch 4/10

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90/90 [==============================] - 105s 1s/step - loss: 0.6959 - accuracy: 0.6950 - val_loss: 0.6424 - val_accuracy: 0.7330
Epoch 5/10

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90/90 [==============================] - 91s 1s/step - loss: 0.6355 - accuracy: 0.7311

90/90 [==============================] - 99s 1s/step - loss: 0.6355 - accuracy: 0.7311 - val_loss: 0.6928 - val_accuracy: 0.7044
Epoch 6/10

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90/90 [==============================] - 92s 1s/step - loss: 0.5776 - accuracy: 0.7574

90/90 [==============================] - 101s 1s/step - loss: 0.5776 - accuracy: 0.7574 - val_loss: 0.6002 - val_accuracy: 0.7466
Epoch 7/10

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90/90 [==============================] - 100s 1s/step - loss: 0.5068 - accuracy: 0.7886 - val_loss: 0.6237 - val_accuracy: 0.7643
Epoch 8/10

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90/90 [==============================] - 92s 1s/step - loss: 0.4663 - accuracy: 0.8150

90/90 [==============================] - 100s 1s/step - loss: 0.4663 - accuracy: 0.8150 - val_loss: 0.5916 - val_accuracy: 0.7616
Epoch 9/10

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90/90 [==============================] - 91s 1s/step - loss: 0.4110 - accuracy: 0.8325

90/90 [==============================] - 100s 1s/step - loss: 0.4110 - accuracy: 0.8325 - val_loss: 0.5823 - val_accuracy: 0.7507
Epoch 10/10

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90/90 [==============================] - 91s 1s/step - loss: 0.3154 - accuracy: 0.8756

90/90 [==============================] - 100s 1s/step - loss: 0.3154 - accuracy: 0.8756 - val_loss: 0.6135 - val_accuracy: 0.7779

# Extract values from the history object
history_values <- as.data.frame(history_vehicle)

# Plot training and validation loss
ggplot(history_values, aes(x = epoch)) +
  geom_line(aes(y = loss, color = "Training Loss")) +
  geom_line(aes(y = val_loss, color = "Validation Loss")) +
  labs(title = "Training and Validation Loss",
       x = "Epoch",
       y = "Loss") +
  scale_color_manual(values = c("Training Loss" = "blue", "Validation Loss" = "red"))

Error in `geom_line()`:
! Problem while computing aesthetics.
ℹ Error occurred in the 1st layer.
Caused by error in `FUN()`:
! object 'loss' not found
Backtrace:
  1. base (local) `<fn>`(x)
  2. ggplot2:::print.ggplot(x)
  4. ggplot2:::ggplot_build.ggplot(x)
  5. ggplot2:::by_layer(...)
 12. ggplot2 (local) f(l = layers[[i]], d = data[[i]])
 13. l$compute_aesthetics(d, plot)
 14. ggplot2 (local) compute_aesthetics(..., self = self)
 15. ggplot2:::scales_add_defaults(...)
 16. base::lapply(aesthetics[new_aesthetics], eval_tidy, data = data)
 17. rlang (local) FUN(X[[i]], ...)

plot(history_vehicle)

# Evaluate the model on the test data
eval_result <- model_vehicle %>% evaluate(test_generator)

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25/25 [==============================] - 9s 342ms/step - loss: 0.6135 - accuracy: 0.7779

25/25 [==============================] - 9s 342ms/step - loss: 0.6135 - accuracy: 0.7779

# Print the test loss
cat("Test accuracy:", eval_result[[2]], "\n")

Test accuracy: 0.7779291 

# Assuming you have already loaded your model and have test data
test_datagen <- image_data_generator(rescale = 1/255)
test_generator <- flow_images_from_directory(
  'C:/CIS8398/flickerimages/test',
  test_datagen,
  target_size = c(150, 150),
  batch_size = 20,
  class_mode = 'categorical'  # Change this to categorical for multi-class
)

Found 734 images belonging to 3 classes.

# Evaluate the model on the test data
eval_result <- model_vehicle %>% evaluate(test_generator)

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37/37 [==============================] - 9s 239ms/step - loss: 1.2936 - accuracy: 0.4210

37/37 [==============================] - 9s 239ms/step - loss: 1.2936 - accuracy: 0.4210

# Print the test loss
cat("Test accuracy:", eval_result[[2]], "\n")

Test accuracy: 0.4209809 

library(keras)
library(reticulate)
library(tidyverse)

# Set your dataset paths for car, ship, and plane
train_dir <- "C:/CIS8398/flickerimages/train"
test_dir <- "C:/CIS8398/flickerimages/test"

model_vehicle1 <- keras_model_sequential() %>%
  layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = "relu",
                input_shape = c(150, 150, 3)) %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_dropout(rate = 0.5) %>%  # Add dropout layer here
  layer_flatten() %>%
  layer_dense(units = 512, activation = "relu") %>%
  layer_dense(units = 3, activation = "sigmoid")

model_vehicle1 %>% compile(
  optimizer = "adam",
  loss = "categorical_crossentropy",  # Use categorical crossentropy for multi-class classification
  metrics = c("accuracy")
)

# Data preprocessing
train_datagen1 <- image_data_generator(
  rescale = 1/255,
  rotation_range = 40,
  width_shift_range = 0.2,
  height_shift_range = 0.2,
  shear_range = 0.2,
  zoom_range = 0.2,
  horizontal_flip = TRUE,
  fill_mode = "nearest"
)

test_datagen1 <- image_data_generator(rescale = 1/255)

train_generator1 <- flow_images_from_directory(
  train_dir,
  train_datagen1,
  target_size = c(150, 150),
  batch_size = 30,
  class_mode = "categorical",
  classes = c("car", "ship", "plane")  # Specify classes for multi-class
)

Error in normalize_path(directory) : object 'train_dir' not found

# Define the learning rate schedule callback
lr_schedule <- callback_reduce_lr_on_plateau(factor = 0.5, patience = 3)

# Fit the model to the data using the generator with the learning rate schedule
history_vehicle <- model_vehicle1 %>%
  fit_generator(
    train_generator1,
    steps_per_epoch = as.integer(90),
    epochs = 10,
    validation_data = test_generator1,
    validation_steps = as.integer(26),
    callbacks = list(lr_schedule)  # Include the learning rate schedule callback here
  )

Warning: `fit_generator` is deprecated. Use `fit` instead, it now accept generators.

Epoch 1/10

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90/90 [==============================] - 72s 799ms/step - loss: 0.7959 - accuracy: 0.6429

WARNING:tensorflow:Learning rate reduction is conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy,lr


90/90 [==============================] - 73s 805ms/step - loss: 0.7959 - accuracy: 0.6429 - lr: 0.0010
Epoch 2/10

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plot(history_vehicle)

library(keras)
library(reticulate)
library(tidyverse)

# Set your dataset paths for car, ship, and plane
train_dir <- "C:/CIS8398/flickerimages/train"
test_dir <- "C:/CIS8398/flickerimages/test"

model_vehicle3 <- keras_model_sequential() %>%
  layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = "relu",
                input_shape = c(150, 150, 3)) %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 128, kernel_size = c(3, 3), activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_dropout(rate = 0.5) %>%  # Add dropout layer here
  layer_flatten() %>%
  layer_dense(units = 512, activation = "relu") %>%
  layer_dense(units = 3, activation = "sigmoid")

model_vehicle3 %>% compile(
  optimizer = "adam",
  loss = "categorical_crossentropy",  # Use categorical crossentropy for multi-class classification
  metrics = c("accuracy")
)

# Data preprocessing
train_datagen3 <- image_data_generator(
  rescale = 1/255,
  rotation_range = 40,
  width_shift_range = 0.2,
  height_shift_range = 0.2,
  shear_range = 0.2,
  zoom_range = 0.2,
  horizontal_flip = TRUE,
  fill_mode = "nearest"
)

test_datagen3 <- image_data_generator(rescale = 1/255)

train_generator3 <- flow_images_from_directory(
  train_dir,
  train_datagen1,
  target_size = c(150, 150),
  batch_size = 30,
  class_mode = "categorical",
  classes = c("car", "ship", "plane")  # Specify classes for multi-class
)

Found 2932 images belonging to 3 classes.

test_generator3 <- flow_images_from_directory(
  test_dir,
  test_datagen1,
  target_size = c(150, 150),
  batch_size = 30,
  class_mode = "categorical",
  classes = c("car", "ship", "plane")  # Specify classes for multi-class
)

Found 734 images belonging to 3 classes.

# Define the learning rate schedule callback
lr_schedule <- callback_reduce_lr_on_plateau(factor = 0.5, patience = 3)

# Fit the model to the data using the generator with the learning rate schedule
history_vehicle3 <- model_vehicle3 %>%
  fit_generator(
    train_generator1,
    steps_per_epoch = as.integer(90),
    epochs = 10,
    validation_data = test_generator,
    validation_steps = as.integer(24),
    callbacks = list(lr_schedule)  # Include the learning rate schedule callback here
  )

Warning: `fit_generator` is deprecated. Use `fit` instead, it now accept generators.

Epoch 1/10

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90/90 [==============================] - 86s 938ms/step - loss: 1.0595 - accuracy: 0.4135 - val_loss: 1.0561 - val_accuracy: 0.4292 - lr: 0.0010
Epoch 2/10

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87/90 [============================>.] - ETA: 2s - loss: 0.9540 - accuracy: 0.5184
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89/90 [============================>.] - ETA: 0s - loss: 0.9533 - accuracy: 0.5146
90/90 [==============================] - 77s 856ms/step - loss: 0.9542 - accuracy: 0.5128

90/90 [==============================] - 86s 951ms/step - loss: 0.9542 - accuracy: 0.5128 - val_loss: 1.2670 - val_accuracy: 0.4319 - lr: 0.0010
Epoch 3/10

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